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AI systems feel smarter than ever. They answer quickly, confidently, and with polish. But beneath that surface, something subtle is going wrong. Outputs are getting safer. Ideas are getting narrower. Surprise is disappearing – less aweful. This matters because AI is increasingly involved in how we search, decide, create, and evaluate. When these systems lose […]
What is Model Collapse? Examples, Causes and Fixes Read More »
AI is taking over the world. If you don’t agree to this, you need to have a look at the latest technologies presented at one of the biggest annual technology events – the CES 2026. Consumer Electronics Show, which takes place in Las Vegas, US, every year, brings forward the best of technologies being pursued
CES 2026: 17 AI Reveals That Will Blow Your Mind Read More »
If you’re curious about trending terms like AI Agents or Agentic AI, you’re in the right place. Agentic AI is rapidly moving from experimentation to enterprise adoption. According to Gartner, over 60% of enterprise AI applications are expected to include agentic components by 2026, while more than 40% of early agentic AI projects are projected to be abandoned due to poor architecture, cost overruns, and lack of
Retrieval-Augmented Generation (RAG) technology almost immediately became the standard in intelligent applications. This was a result of the quickly developing field of artificial intelligence that combined large language models and external knowledge bases with different real-time access methods. RAG implementation of the traditional kind poses major difficulties: complex vector database setups, intricate embedding pathways, orchestration
NyRAG: Building Production-Ready RAG Applications with Zero Code Read More »
The name Google has always been synonymous with technology, and things are no different in the age of AI. Google has quietly been the frontrunner in the AI revolution with a host of products that surprisingly few people know about. Of course, the showstoppers like Gemini and NotebookLM have been popular, but their capabilities have
Most RAG demos stop at “upload a PDF and ask a question.” That proves the pipeline works. It doesn’t prove you understand it. These projects are designed to break in interesting ways. They surface bias, contradictions, forgotten context, and overconfident answers. That’s where real RAG learning starts. Once you’re through these, you would have an
10 RAG Projects That Actually Teach You Retrieval Read More »
The beginning of a new year brings about a new sense of energy in most. One may argue that it is all psychological, as nothing changes other than the date. Agreed, to a point. Though it is psychological, the change is not just based on the onset of a “new year.” A deep-rooted logical reasoning
Win 2026! 9 AI Prompts to Enter Beast Mode This New Year Read More »
Learning Python at the beginning feels deceptively simple. You write a few lines, the code runs, and it’s tempting to think you’ve got it. Then you try to build something on your own and… nothing works!? Turns out all the information you had learnt, didn’t find an outlet. That’s where challenging projects matter. Not flashy
In machine learning with categorical data, it is common to encode the categories as dummy variables (sometimes called one hot encoding) to encode categories as numerical values. This is a significant step since there are many algorithms that do not operate on other things other than numbers like linear regression. Nevertheless, there is one of the mistakes
Dummy Variable Trap in Machine Learning Explained Simply Read More »
Data science powers decision-making across modern businesses, from data preparation and automation to advanced analytics and machine learning. Learning it requires a strong foundation in mathematics, statistics, programming, and practical problem-solving. The good news is that data science can be self-learned with the right resources and consistent practice. Books remain one of the most effective
